Be the JSON file

{
  "students": [
    {
      "id": 19091,
      "firstName": "Peter",
      "lastName": "Mueller",
      "grades": {
          "micro": 5,
          "macro": 4.5,
          "data handling": 5.5
          }
    },
    {
      "id": 19092,
      "firstName": "Anna",
      "lastName": "Schmid",
      "grades": {
          "micro": 5.25,
          "macro": 4,
          "data handling": 5.75
          }
    },
    {
      "id": 19093,
      "firstName": "Noah",
      "lastName": "Trevor",
      "grades": {
          "micro": 4,
          "macro": 4.5,
          "data handling": 5
          }
    }
  ]
}

Write an R code to extract a table with, as a first column, a vector of first names, and as a second column, the average grade per student. The table can be a data frame or a tibble.

<students>
  <student>
    <id>19091</id>
    <firstName>Peter</firstName>
    <lastName>Mueller</lastName>
    <grades>
      <micro>5</micro>
      <macro>4.5</macro>
      <dataHandling>5.5</dataHandling>
    </grades>
  </student>
  <student>
    <id>19092</id>
    <firstName>Anna</firstName>
    <lastName>Schmid</lastName>
    <grades>
      <micro>5.25</micro>
      <macro>4</macro>
      <dataHandling>5.75</dataHandling>
    </grades>
  </student>
  <student>
    <id>19093</id>
    <firstName>Noah</firstName>
    <lastName>Trevor</lastName>
    <grades>
      <micro>4</micro>
      <macro>4.5</macro>
      <dataHandling>5</dataHandling>
    </grades>
  </student>
</students>

• ‘students’ is the root-node, ‘grades’ are its children
• the siblings of Noah Trevor are Anna Schmid and Peter Mueller
• The code below would be an alternative, equivalent notation for the third student in the xml file above.

<student id="19093" firstName="Noah" lastName="Trevor">
      <grades micro="4" macro="4.5" dataHandling="5" />
</student>

Tell your future self what this script is all about

#######################################################################
# Project XY: Data Gathering and Import
#
# This script is the first part of the data pipeline of project XY.
# It imports data from ...
# Input: links to data sources (data comes in ... format)
# Output: cleaned data as CSV
# 
# U. Matter, St. Gallen, 2018
#######################################################################


# SET UP --------------
# load packages
library(tidyverse)

# set fix variables
INPUT_PATH <- "/rawdata"
OUTPUT_FILE <- "/final_data/datafile.csv"


# IMPORT RAW DATA FROM CSVs -------------

• Recognize where the problems are in a given dataset, and what is in the way of a proper analysis of the data.
• Organize your work: what needs to be addressed first?

• Use simple string-operations to clean text variables.
• Reshape datasets from wide to long (and vice versa).
• Apply row-binding/stacking of datasets

• In practice: still a long way to go.
• Parsable, but messy data: Inconsistencies, data types, missing observations, wide format.

• In practice: still a long way to go.
• Parsable, but messy data: Inconsistencies, data types, missing observations, wide format.
• Goal of data preparation: Dataset is ready for analysis.
• Key conditions:
  1. Data values are consistent/clean within each variable.
  2. Variables are of proper data types.
  3. Dataset is in ‘tidy’ (in long format, more on this after the break)!

Following Wickham (2014):

• Dataset: Collection of values (numbers and strings).
• Every value belongs to a variable and an observation.
• Variable: Contains all values that measure the same underlying attribute across units.
• Observation: Contains all values measured on the same unit (e.g., a person).